Co-Parenting for Successful Kids: Impacts and Implications

We examined the impacts of the Co-Parenting for Successful Kids program offered by University of Nebraska Extension. Using a sample of 2,622 parents who participated in the program in 2015, we measured their knowledge change and ability to perform cooperative coparenting behaviors. Results suggest that the program effectively improved participants\u27 coparenting knowledge and ability to use behaviors such as understanding and supporting children in developmentally appropriate ways, enhancing communication skills, and developing parenting plans. In examining group differences, we found that parents of infants and toddlers benefited the most from the program. Suggestions on program development and evaluation are discussed

A Qualitative Evaluation to Improve the Co-Parenting for Successful Kids Program

Programs aiming to help parents are often challenged in analyzing open-ended survey questions from large samples. This article presents qualitative findings collected from 1,287 participants with a child 5 years of age or younger who completed the program evaluation for the Co-Parenting for Successful Kids online program, a 4-hour education course developed by the University of Nebraska Extension. Qualitative content analysis revealed that participants found the program useful for improving their co-parenting communication skills. Participants suggested areas for improvement such as additional information for helping children cope, conflict resolution strategies, handling legal issues, and understanding how divorce impacts children based on their age. Supports and information were requested from parents in high conflict situations, including families dealing with a co-parent’s alcohol and drug abuse, domestic violence, and having an uninvolved or absent parent. Analyzing qualitative data from participants and quantifying these responses into themes offers a useful and informative way to improve and enhance an existing education program aiming to support separating or divorcing parents

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

Dust and molecular shells in asymptotic giant branch stars - Mid-infrared interferometric observations of R Aql, R Aqr, R Hya, W Hya and V Hya

Mid-IR (8 - 13 micron) interferometric data of four oxygen-rich AGB stars (R Aql, R Aqr, R Hya, and W Hya) and one carbon-rich AGB star (V Hya) were obtained with MIDI/VLTI between April 2007 and September 2009. The spectrally dispersed visibility data are analyzed by fitting a circular fully limb-darkened disk (FDD). Results. The FDD diameter as function of wavelength is similar for all oxygen-rich stars. The apparent size is almost constant between 8 and 10 micron and gradually increases at wavelengths longer than 10 micron. The apparent FDD diameter in the carbon-rich star V Hya essentially decreases from 8 to 12 micron. The FDD diameters are about 2.2 times larger than the photospheric diameters estimated from K-band observations found in the literature. The silicate dust shells of R Aql, R Hya and W Hya are located fairly far away from the star, while the silicate dust shell of R Aqr and the amorphous carbon (AMC) and SiC dust shell of V Hya are found to be closer to the star at around 8 photospheric radii. Phase-to-phase variations of the diameters of the oxygen-rich stars could be measured and are on the order of 15% but with large uncertainties. From a comparison of the diameter trend with the trends in RR Sco and S Ori it can be concluded that in oxygen-rich stars the overall larger diameter originates from a warm molecular layer of H2O, and the gradual increase longward of 10 micron can be most likely attributed to the contribution of a close Al2O3 dust shell. The chromatic trend of the Gaussian FWHM in V Hya can be explained with the presence of AMC and SiC dust. The observations suggest that the formation of amorphous Al2O3 in oxygen- rich stars occurs mainly around or after visual minimum. However, no firm conclusions can be drawn concerning the mass-loss mechanism.Comment: 32 pages (including 7 pages appendix), 10 figure

Early ultrasound surveillance of newly-created haemodialysis arteriovenous fistula

IntroductionWe assess if ultrasound surveillance of newly-created arteriovenous fistulas (AVFs) can predict nonmaturation sufficiently reliably to justify randomized controlled trial (RCT) evaluation of ultrasound-directed salvage intervention.MethodsConsenting adults underwent blinded fortnightly ultrasound scanning of their AVF after creation, with scan characteristics that predicted AVF nonmaturation identified by logistic regression modeling.ResultsOf 333 AVFs created, 65.8% matured by 10 weeks. Serial scanning revealed that maturation occurred rapidly, whereas consistently lower fistula flow rates and venous diameters were observed in those that did not mature. Wrist and elbow AVF nonmaturation could be optimally modeled from week 4 ultrasound parameters alone, but with only moderate positive predictive values (PPVs) (wrist, 60.6% [95% confidence interval, CI: 43.9–77.3]; elbow, 66.7% [48.9–84.4]). Moreover, 40 (70.2%) of the 57 AVFs that thrombosed by week 10 had already failed by the week 4 scan, thus limiting the potential of salvage procedures initiated by that scan’s findings to alter overall maturation rates. Modeling of the early ultrasound characteristics could also predict primary patency failure at 6 months; however, that model performed poorly at predicting assisted primary failure (those AVFs that failed despite a salvage attempt), partly because patency of at-risk AVFs was maintained by successful salvage performed without recourse to the early scan data.ConclusionEarly ultrasound surveillance may predict fistula maturation, but is likely, at best, to result in only very modest improvements in fistula patency. Power calculations suggest that an impractically large number of participants (>1700) would be required for formal RCT evaluation

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Maternal and paternal genomes differentially affect myofibre characteristics and muscle weights of bovine fetuses at midgestation

Postnatal myofibre characteristics and muscle mass are largely determined during fetal development and may be significantly affected by epigenetic parent-of-origin effects. However, data on such effects in prenatal muscle development that could help understand unexplained variation in postnatal muscle traits are lacking. In a bovine model we studied effects of distinct maternal and paternal genomes, fetal sex, and non-genetic maternal effects on fetal myofibre characteristics and muscle mass. Data from 73 fetuses (Day153, 54% term) of four genetic groups with purebred and reciprocal cross Angus and Brahman genetics were analyzed using general linear models. Parental genomes explained the greatest proportion of variation in myofibre size of Musculus semitendinosus (80–96%) and in absolute and relative weights of M. supraspinatus, M. longissimus dorsi, M. quadriceps femoris and M. semimembranosus (82–89% and 56–93%, respectively). Paternal genome in interaction with maternal genome (P<0.05) explained most genetic variation in cross sectional area (CSA) of fast myotubes (68%), while maternal genome alone explained most genetic variation in CSA of fast myofibres (93%, P<0.01). Furthermore, maternal genome independently (M. semimembranosus, 88%, P<0.0001) or in combination (M. supraspinatus, 82%; M. longissimus dorsi, 93%; M. quadriceps femoris, 86%) with nested maternal weight effect (5–6%, P<0.05), was the predominant source of variation for absolute muscle weights. Effects of paternal genome on muscle mass decreased from thoracic to pelvic limb and accounted for all (M. supraspinatus, 97%, P<0.0001) or most (M. longissimus dorsi, 69%, P<0.0001; M. quadriceps femoris, 54%, P<0.001) genetic variation in relative weights. An interaction between maternal and paternal genomes (P<0.01) and effects of maternal weight (P<0.05) on expression of H19, a master regulator of an imprinted gene network, and negative correlations between H19 expression and fetal muscle mass (P<0.001), suggested imprinted genes and miRNA interference as mechanisms for differential effects of maternal and paternal genomes on fetal muscle.Ruidong Xiang, Mani Ghanipoor-Samami, William H. Johns, Tanja Eindorf, David L. Rutley, Zbigniew A. Kruk, Carolyn J. Fitzsimmons, Dana A. Thomsen, Claire T. Roberts, Brian M. Burns, Gail I. Anderson, Paul L. Greenwood, Stefan Hiendlede

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease